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<h1 class="title"><a class="reference external" href="http://www.osl.iu.edu/research/pbgl"><img align="middle" alt="Parallel BGL" class="align-middle" src="pbgl-logo.png" /></a> Minimum Spanning Tree</h1>

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<p>The Parallel BGL contains four <a class="reference external" href="http://www.boost.org/libs/graph/doc/graph_theory_review.html#sec:minimum-spanning-tree">minimum spanning tree</a> (MST)
algorithms <a class="citation-reference" href="#dg98" id="id1">[DG98]</a> for use on undirected, weighted, distributed
graphs. The graphs need not be connected: each algorithm will compute
a minimum spanning forest (MSF) when provided with a disconnected
graph.</p>
<p>The interface to each of the four algorithms is similar to the
implementation of 'Kruskal's algorithm'_ in the sequential BGL. Each
accepts, at a minimum, a graph, a weight map, and an output
iterator. The edges of the MST (or MSF) will be output via the output
iterator on process 0: other processes may receive edges on their
output iterators, but the set may not be complete, depending on the
algorithm. The algorithm parameters are documented together, because
they do not vary greatly. See the section <a class="reference internal" href="#selecting-an-mst-algorithm">Selecting an MST
algorithm</a> for advice on algorithm selection.</p>
<p>The graph itself must model the <a class="reference external" href="http://www.boost.org/libs/graph/doc/VertexListGraph.html">Vertex List Graph</a> concept and the
Distributed Edge List Graph concept. Since the most common
distributed graph structure, the <a class="reference external" href="distributed_adjacency_list.html">distributed adjacency list</a>, only
models the Distributed Vertex List Graph concept, it may only be used
with these algorithms when wrapped in a suitable adaptor, such as the
<a class="reference external" href="vertex_list_adaptor.html">vertex_list_adaptor</a>.</p>
<div class="contents topic" id="contents">
<p class="topic-title first">Contents</p>
<ul class="simple">
<li><a class="reference internal" href="#where-defined" id="id12">Where Defined</a></li>
<li><a class="reference internal" href="#parameters" id="id13">Parameters</a></li>
<li><a class="reference internal" href="#dense-boruvka-minimum-spanning-tree" id="id14"><tt class="docutils literal"><span class="pre">dense_boruvka_minimum_spanning_tree</span></tt></a><ul>
<li><a class="reference internal" href="#description" id="id15">Description</a></li>
<li><a class="reference internal" href="#complexity" id="id16">Complexity</a></li>
<li><a class="reference internal" href="#performance" id="id17">Performance</a></li>
</ul>
</li>
<li><a class="reference internal" href="#merge-local-minimum-spanning-trees" id="id18"><tt class="docutils literal"><span class="pre">merge_local_minimum_spanning_trees</span></tt></a><ul>
<li><a class="reference internal" href="#id2" id="id19">Description</a></li>
<li><a class="reference internal" href="#id3" id="id20">Complexity</a></li>
<li><a class="reference internal" href="#id4" id="id21">Performance</a></li>
</ul>
</li>
<li><a class="reference internal" href="#boruvka-then-merge" id="id22"><tt class="docutils literal"><span class="pre">boruvka_then_merge</span></tt></a><ul>
<li><a class="reference internal" href="#id5" id="id23">Description</a></li>
<li><a class="reference internal" href="#id6" id="id24">Complexity</a></li>
<li><a class="reference internal" href="#id7" id="id25">Performance</a></li>
</ul>
</li>
<li><a class="reference internal" href="#boruvka-mixed-merge" id="id26"><tt class="docutils literal"><span class="pre">boruvka_mixed_merge</span></tt></a><ul>
<li><a class="reference internal" href="#id8" id="id27">Description</a></li>
<li><a class="reference internal" href="#id9" id="id28">Complexity</a></li>
<li><a class="reference internal" href="#id10" id="id29">Performance</a></li>
</ul>
</li>
<li><a class="reference internal" href="#selecting-an-mst-algorithm" id="id30">Selecting an MST algorithm</a><ul>
<li><a class="reference internal" href="#performance-on-sparse-graphs" id="id31">Performance on Sparse Graphs</a></li>
<li><a class="reference internal" href="#performance-on-dense-graphs" id="id32">Performance on Dense Graphs</a></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="where-defined">
<h1><a class="toc-backref" href="#id12">Where Defined</a></h1>
<p>&lt;<tt class="docutils literal"><span class="pre">boost/graph/distributed/dehne_gotz_min_spanning_tree.hpp</span></tt>&gt;</p>
</div>
<div class="section" id="parameters">
<h1><a class="toc-backref" href="#id13">Parameters</a></h1>
<dl class="docutils">
<dt>IN: <tt class="docutils literal"><span class="pre">Graph&amp;</span> <span class="pre">g</span></tt></dt>
<dd>The graph type must be a model of <a class="reference external" href="http://www.boost.org/libs/graph/doc/VertexListGraph.html">Vertex List Graph</a> and
<a class="reference external" href="DistributedEdgeListGraph.html">Distributed Edge List Graph</a>.</dd>
<dt>IN/OUT: <tt class="docutils literal"><span class="pre">WeightMap</span> <span class="pre">weight</span></tt></dt>
<dd>The weight map must be a <a class="reference external" href="distributed_property_map.html">Distributed Property Map</a> and a <a class="reference external" href="http://www.boost.org/libs/property_map/ReadablePropertyMap.html">Readable
Property Map</a> whose key type is the edge descriptor of the graph
and whose value type is numerical.</dd>
<dt>IN/OUT: <tt class="docutils literal"><span class="pre">OutputIterator</span> <span class="pre">out</span></tt></dt>
<dd>The output iterator through which the edges of the MSF will be
written. Must be capable of accepting edge descriptors for output.</dd>
<dt>IN: <tt class="docutils literal"><span class="pre">VertexIndexMap</span> <span class="pre">index</span></tt></dt>
<dd><p class="first">A mapping from vertex descriptors to indices in the range <em>[0,
num_vertices(g))</em>. This must be a <a class="reference external" href="http://www.boost.org/libs/property_map/ReadablePropertyMap.html">Readable Property Map</a> whose
key type is a vertex descriptor and whose value type is an integral
type, typically the <tt class="docutils literal"><span class="pre">vertices_size_type</span></tt> of the graph.</p>
<p class="last"><strong>Default:</strong> <tt class="docutils literal"><span class="pre">get(vertex_index,</span> <span class="pre">g)</span></tt></p>
</dd>
<dt>IN/UTIL: <tt class="docutils literal"><span class="pre">RankMap</span> <span class="pre">rank_map</span></tt></dt>
<dd><p class="first">Stores the rank of each vertex, which is used for maintaining
union-find data structures. This must be a <a class="reference external" href="http://www.boost.org/libs/property_map/ReadWritePropertyMap.html">Read/Write Property Map</a>
whose key type is a vertex descriptor and whose value type is an
integral type.</p>
<p class="last"><strong>Default:</strong> An <tt class="docutils literal"><span class="pre">iterator_property_map</span></tt> built from an STL vector
of the <tt class="docutils literal"><span class="pre">vertices_size_type</span></tt> of the graph and the vertex index map.</p>
</dd>
<dt>IN/UTIL: <tt class="docutils literal"><span class="pre">ParentMap</span> <span class="pre">parent_map</span></tt></dt>
<dd><p class="first">Stores the parent (representative) of each vertex, which is used for
maintaining union-find data structures. This must be a <a class="reference external" href="http://www.boost.org/libs/property_map/ReadWritePropertyMap.html">Read/Write
Property Map</a> whose key type is a vertex descriptor and whose value
type is also a vertex descriptor.</p>
<p class="last"><strong>Default:</strong> An <tt class="docutils literal"><span class="pre">iterator_property_map</span></tt> built from an STL vector
of the <tt class="docutils literal"><span class="pre">vertex_descriptor</span></tt> of the graph and the vertex index map.</p>
</dd>
<dt>IN/UTIL: <tt class="docutils literal"><span class="pre">SupervertexMap</span> <span class="pre">supervertex_map</span></tt></dt>
<dd><p class="first">Stores the supervertex index of each vertex, which is used for
maintaining the supervertex list data structures. This must be a
<a class="reference external" href="http://www.boost.org/libs/property_map/ReadWritePropertyMap.html">Read/Write Property Map</a> whose key type is a vertex descriptor and
whose value type is an integral type.</p>
<p class="last"><strong>Default:</strong> An <tt class="docutils literal"><span class="pre">iterator_property_map</span></tt> built from an STL vector
of the <tt class="docutils literal"><span class="pre">vertices_size_type</span></tt> of the graph and the vertex index map.</p>
</dd>
</dl>
</div>
<div class="section" id="dense-boruvka-minimum-spanning-tree">
<h1><a class="toc-backref" href="#id14"><tt class="docutils literal"><span class="pre">dense_boruvka_minimum_spanning_tree</span></tt></a></h1>
<pre class="literal-block">
namespace graph {
  template&lt;typename Graph, typename WeightMap, typename OutputIterator,
           typename VertexIndexMap, typename RankMap, typename ParentMap,
           typename SupervertexMap&gt;
  OutputIterator
  dense_boruvka_minimum_spanning_tree(const Graph&amp; g, WeightMap weight_map,
                                    OutputIterator out,
                                    VertexIndexMap index,
                                    RankMap rank_map, ParentMap parent_map,
                                    SupervertexMap supervertex_map);

  template&lt;typename Graph, typename WeightMap, typename OutputIterator,
           typename VertexIndex&gt;
  OutputIterator
  dense_boruvka_minimum_spanning_tree(const Graph&amp; g, WeightMap weight_map,
                                    OutputIterator out, VertexIndex index);

  template&lt;typename Graph, typename WeightMap, typename OutputIterator&gt;
  OutputIterator
  dense_boruvka_minimum_spanning_tree(const Graph&amp; g, WeightMap weight_map,
                                    OutputIterator out);
}
</pre>
<div class="section" id="description">
<h2><a class="toc-backref" href="#id15">Description</a></h2>
<p>The dense Boruvka distributed minimum spanning tree algorithm is a
direct parallelization of the sequential MST algorithm by
Boruvka. The algorithm first creates a <em>supervertex</em> out of each
vertex. Then, in each iteration, it finds the smallest-weight edge
incident to each vertex, collapses supervertices along these edges,
and removals all self loops. The only difference between the
sequential and parallel algorithms is that the parallel algorithm
performs an all-reduce operation so that all processes have the
global minimum set of edges.</p>
<p>Unlike the other three algorithms, this algorithm emits the complete
list of edges in the minimum spanning forest via the output iterator
on all processes. It may therefore be more useful than the others
when parallelizing sequential BGL programs.</p>
</div>
<div class="section" id="complexity">
<h2><a class="toc-backref" href="#id16">Complexity</a></h2>
<p>The distributed algorithm requires <em>O(log n)</em> BSP supersteps, each of
which requires <em>O(m/p + n)</em> time and <em>O(n)</em> communication per
process.</p>
</div>
<div class="section" id="performance">
<h2><a class="toc-backref" href="#id17">Performance</a></h2>
<p>The following charts illustrate the performance of this algorithm on
various random graphs. We see that the algorithm scales well up to 64
or 128 processors, depending on the type of graph, for dense
graphs. However, for sparse graphs performance tapers off as the
number of processors surpases <em>m/n</em>, i.e., the average degree (which
is 30 for this graph). This behavior is expected from the algorithm.</p>
<img align="left" alt="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_5.png" class="align-left" src="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_5.png" />
<img alt="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_5_speedup_1.png" src="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_5_speedup_1.png" />
<img align="left" alt="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_5.png" class="align-left" src="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_5.png" />
<img alt="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_5_speedup_1.png" src="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_5_speedup_1.png" />
</div>
</div>
<div class="section" id="merge-local-minimum-spanning-trees">
<h1><a class="toc-backref" href="#id18"><tt class="docutils literal"><span class="pre">merge_local_minimum_spanning_trees</span></tt></a></h1>
<pre class="literal-block">
namespace graph {
  template&lt;typename Graph, typename WeightMap, typename OutputIterator,
           typename VertexIndexMap&gt;
  OutputIterator
  merge_local_minimum_spanning_trees(const Graph&amp; g, WeightMap weight,
                                     OutputIterator out,
                                     VertexIndexMap index);

  template&lt;typename Graph, typename WeightMap, typename OutputIterator&gt;
  inline OutputIterator
  merge_local_minimum_spanning_trees(const Graph&amp; g, WeightMap weight,
                                     OutputIterator out);
}
</pre>
<div class="section" id="id2">
<h2><a class="toc-backref" href="#id19">Description</a></h2>
<p>The merging local MSTs algorithm operates by computing minimum
spanning forests from the edges stored on each process. Then the
processes merge their edge lists along a tree. The child nodes cease
participating in the computation, but the parent nodes recompute MSFs
from the newly acquired edges. In the final round, the root of the
tree computes the global MSFs, having received candidate edges from
every other process via the tree.</p>
</div>
<div class="section" id="id3">
<h2><a class="toc-backref" href="#id20">Complexity</a></h2>
<p>This algorithm requires <em>O(log_D p)</em> BSP supersteps (where <em>D</em> is the
number of children in the tree, and is currently fixed at 3). Each
superstep requires <em>O((m/p) log (m/p) + n)</em> time and <em>O(m/p)</em>
communication per process.</p>
</div>
<div class="section" id="id4">
<h2><a class="toc-backref" href="#id21">Performance</a></h2>
<p>The following charts illustrate the performance of this algorithm on
various random graphs. The algorithm only scales well for very dense
graphs, where most of the work is performed in the initial stage and
there is very little work in the later stages.</p>
<img align="left" alt="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_6.png" class="align-left" src="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_6.png" />
<img alt="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_6_speedup_1.png" src="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_6_speedup_1.png" />
<img align="left" alt="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_6.png" class="align-left" src="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_6.png" />
<img alt="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_6_speedup_1.png" src="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_6_speedup_1.png" />
</div>
</div>
<div class="section" id="boruvka-then-merge">
<h1><a class="toc-backref" href="#id22"><tt class="docutils literal"><span class="pre">boruvka_then_merge</span></tt></a></h1>
<pre class="literal-block">
namespace graph {
  template&lt;typename Graph, typename WeightMap, typename OutputIterator,
           typename VertexIndexMap, typename RankMap, typename ParentMap,
           typename SupervertexMap&gt;
  OutputIterator
  boruvka_then_merge(const Graph&amp; g, WeightMap weight, OutputIterator out,
                     VertexIndexMap index, RankMap rank_map,
                     ParentMap parent_map, SupervertexMap
                     supervertex_map);

  template&lt;typename Graph, typename WeightMap, typename OutputIterator,
           typename VertexIndexMap&gt;
  inline OutputIterator
  boruvka_then_merge(const Graph&amp; g, WeightMap weight, OutputIterator out,
                      VertexIndexMap index);

  template&lt;typename Graph, typename WeightMap, typename OutputIterator&gt;
  inline OutputIterator
  boruvka_then_merge(const Graph&amp; g, WeightMap weight, OutputIterator out);
}
</pre>
<div class="section" id="id5">
<h2><a class="toc-backref" href="#id23">Description</a></h2>
<p>This algorithm applies both Boruvka steps and local MSF merging steps
together to achieve better asymptotic performance than either
algorithm alone. It first executes Boruvka steps until only <em>n/(log_d
p)^2</em> supervertices remain, then completes the MSF computation by
performing local MSF merging on the remaining edges and
supervertices.</p>
</div>
<div class="section" id="id6">
<h2><a class="toc-backref" href="#id24">Complexity</a></h2>
<p>This algorithm requires <em>log_D p</em> + <em>log log_D p</em> BSP supersteps. The
time required by each superstep depends on the type of superstep
being performed; see the distributed Boruvka or merging local MSFS
algorithms for details.</p>
</div>
<div class="section" id="id7">
<h2><a class="toc-backref" href="#id25">Performance</a></h2>
<p>The following charts illustrate the performance of this algorithm on
various random graphs. We see that the algorithm scales well up to 64
or 128 processors, depending on the type of graph, for dense
graphs. However, for sparse graphs performance tapers off as the
number of processors surpases <em>m/n</em>, i.e., the average degree (which
is 30 for this graph). This behavior is expected from the algorithm.</p>
<img align="left" alt="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_7.png" class="align-left" src="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_7.png" />
<img alt="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_7_speedup_1.png" src="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_7_speedup_1.png" />
<img align="left" alt="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_7.png" class="align-left" src="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_7.png" />
<img alt="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_7_speedup_1.png" src="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_7_speedup_1.png" />
</div>
</div>
<div class="section" id="boruvka-mixed-merge">
<h1><a class="toc-backref" href="#id26"><tt class="docutils literal"><span class="pre">boruvka_mixed_merge</span></tt></a></h1>
<pre class="literal-block">
namespace {
  template&lt;typename Graph, typename WeightMap, typename OutputIterator,
           typename VertexIndexMap, typename RankMap, typename ParentMap,
           typename SupervertexMap&gt;
  OutputIterator
  boruvka_mixed_merge(const Graph&amp; g, WeightMap weight, OutputIterator out,
                      VertexIndexMap index, RankMap rank_map,
                      ParentMap parent_map, SupervertexMap
                      supervertex_map);

  template&lt;typename Graph, typename WeightMap, typename OutputIterator,
           typename VertexIndexMap&gt;
  inline OutputIterator
  boruvka_mixed_merge(const Graph&amp; g, WeightMap weight, OutputIterator out,
                      VertexIndexMap index);

  template&lt;typename Graph, typename WeightMap, typename OutputIterator&gt;
  inline OutputIterator
  boruvka_mixed_merge(const Graph&amp; g, WeightMap weight, OutputIterator out);
}
</pre>
<div class="section" id="id8">
<h2><a class="toc-backref" href="#id27">Description</a></h2>
<p>This algorithm applies both Boruvka steps and local MSF merging steps
together to achieve better asymptotic performance than either method
alone. In each iteration, the algorithm first performs a Boruvka step
and then merges the local MSFs computed based on the supervertex
graph.</p>
</div>
<div class="section" id="id9">
<h2><a class="toc-backref" href="#id28">Complexity</a></h2>
<p>This algorithm requires <em>log_D p</em> BSP supersteps. The
time required by each superstep depends on the type of superstep
being performed; see the distributed Boruvka or merging local MSFS
algorithms for details. However, the algorithm is
communication-optional (requiring <em>O(n)</em> communication overall) when
the graph is sufficiently dense, i.e., <em>m/n &gt;= p</em>.</p>
</div>
<div class="section" id="id10">
<h2><a class="toc-backref" href="#id29">Performance</a></h2>
<p>The following charts illustrate the performance of this algorithm on
various random graphs. We see that the algorithm scales well up to 64
or 128 processors, depending on the type of graph, for dense
graphs. However, for sparse graphs performance tapers off as the
number of processors surpases <em>m/n</em>, i.e., the average degree (which
is 30 for this graph). This behavior is expected from the algorithm.</p>
<img align="left" alt="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_8.png" class="align-left" src="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_8.png" />
<img alt="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_8_speedup_1.png" src="chart_php_generator_ER_SF_SW_dataset_TimeSparse_columns_8_speedup_1.png" />
<img align="left" alt="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_8.png" class="align-left" src="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_8.png" />
<img alt="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_8_speedup_1.png" src="chart_php_generator_ER_SF_SW_dataset_TimeDense_columns_8_speedup_1.png" />
</div>
</div>
<div class="section" id="selecting-an-mst-algorithm">
<h1><a class="toc-backref" href="#id30">Selecting an MST algorithm</a></h1>
<p>Dehne and Gotz reported <a class="citation-reference" href="#dg98" id="id11">[DG98]</a> mixed results when evaluating these
four algorithms. No particular algorithm was clearly better than the
others in all cases. However, the asymptotically best algorithm
(<tt class="docutils literal"><span class="pre">boruvka_mixed_merge</span></tt>) seemed to perform more poorly in their tests
than the other merging-based algorithms. The following performance
charts illustrate the performance of these four minimum spanning tree
implementations.</p>
<p>Overall, <tt class="docutils literal"><span class="pre">dense_boruvka_minimum_spanning_tree</span></tt> gives the most
consistent performance and scalability for the graphs we
tested. Additionally, it may be more suitable for sequential programs
that are being parallelized, because it emits complete MSF edge lists
via the output iterators in every process.</p>
<div class="section" id="performance-on-sparse-graphs">
<h2><a class="toc-backref" href="#id31">Performance on Sparse Graphs</a></h2>
<img align="left" alt="chart_php_generator_ER_dataset_TimeSparse_columns_5_6_7_8.png" class="align-left" src="chart_php_generator_ER_dataset_TimeSparse_columns_5_6_7_8.png" />
<img alt="chart_php_generator_ER_dataset_TimeSparse_columns_5_6_7_8_speedup_1.png" src="chart_php_generator_ER_dataset_TimeSparse_columns_5_6_7_8_speedup_1.png" />
<img align="left" alt="chart_php_generator_SF_dataset_TimeSparse_columns_5_6_7_8.png" class="align-left" src="chart_php_generator_SF_dataset_TimeSparse_columns_5_6_7_8.png" />
<img alt="chart_php_generator_SF_dataset_TimeSparse_columns_5_6_7_8_speedup_1.png" src="chart_php_generator_SF_dataset_TimeSparse_columns_5_6_7_8_speedup_1.png" />
<img align="left" alt="chart_php_generator_SW_dataset_TimeSparse_columns_5_6_7_8.png" class="align-left" src="chart_php_generator_SW_dataset_TimeSparse_columns_5_6_7_8.png" />
<img alt="chart_php_generator_SW_dataset_TimeSparse_columns_5_6_7_8_speedup_1.png" src="chart_php_generator_SW_dataset_TimeSparse_columns_5_6_7_8_speedup_1.png" />
</div>
<div class="section" id="performance-on-dense-graphs">
<h2><a class="toc-backref" href="#id32">Performance on Dense Graphs</a></h2>
<img align="left" alt="chart_php_generator_ER_dataset_TimeDense_columns_5_6_7_8.png" class="align-left" src="chart_php_generator_ER_dataset_TimeDense_columns_5_6_7_8.png" />
<img alt="chart_php_generator_ER_dataset_TimeDense_columns_5_6_7_8_speedup_1.png" src="chart_php_generator_ER_dataset_TimeDense_columns_5_6_7_8_speedup_1.png" />
<img align="left" alt="chart_php_generator_SF_dataset_TimeDense_columns_5_6_7_8.png" class="align-left" src="chart_php_generator_SF_dataset_TimeDense_columns_5_6_7_8.png" />
<img alt="chart_php_generator_SF_dataset_TimeDense_columns_5_6_7_8_speedup_1.png" src="chart_php_generator_SF_dataset_TimeDense_columns_5_6_7_8_speedup_1.png" />
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<p>Copyright (C) 2004 The Trustees of Indiana University.</p>
<p>Authors: Douglas Gregor and Andrew Lumsdaine</p>
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<tr><td class="label">[DG98]</td><td><em>(<a class="fn-backref" href="#id1">1</a>, <a class="fn-backref" href="#id11">2</a>)</em> Frank Dehne and Silvia Gotz. <em>Practical Parallel Algorithms
for Minimum Spanning Trees</em>. In Symposium on Reliable Distributed Systems,
pages 366--371, 1998.</td></tr>
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